# simple

## SimpleCycle

Runs an experimentalist, theorist and experiment runner in a loop.

Once initialized, the cycle can be started using the cycle.run method or by calling next(cycle).

The .data attribute is updated with the results.

Attributes:

Name Type Description
data dataclass

an object which is updated during the cycle and has the following properties:

• metadata
• conditions: a list of np.ndarrays representing all of the IVs proposed by the experimentalist
• observations: a list of np.ndarrays representing all of the IVs and DVs returned by the experiment runner
• theories: a list of all the fitted theories (scikit-learn compatible estimators)
params dict

a nested dictionary with parameters for the cycle parts.

{
"experimentalist": {<experimentalist params...>},
"theorist": {<theorist params...>},
"experiment_runner": {<experiment_runner params...>}
}


Examples:

##### Basic Usage

Aim: Use the SimpleCycle to recover a simple ground truth theory from noisy data.

>>> def ground_truth(x):
...     return x + 1


The space of allowed x values is the integers between 0 and 10 inclusive, and we record the allowed output values as well.

>>> from autora.variable import VariableCollection, Variable
>>> metadata_0 = VariableCollection(
...    independent_variables=[Variable(name="x1", allowed_values=range(11))],
...    dependent_variables=[Variable(name="y", value_range=(-20, 20))],
...    )


The experimentalist is used to propose experiments. Since the space of values is so restricted, we can just sample them all each time.

>>> from autora.experimentalist.pipeline import make_pipeline
>>> example_experimentalist = make_pipeline(


When we run a synthetic experiment, we get a reproducible noisy result:

>>> import numpy as np
>>> def get_example_synthetic_experiment_runner():
...     rng = np.random.default_rng(seed=180)
...     def runner(x):
...         return ground_truth(x) + rng.normal(0, 0.1, x.shape)
...     return runner
>>> example_synthetic_experiment_runner = get_example_synthetic_experiment_runner()
>>> example_synthetic_experiment_runner(np.ndarray([1]))
array([2.04339546])


The theorist "tries" to work out the best theory. We use a trivial scikit-learn regressor.

>>> from sklearn.linear_model import LinearRegression
>>> example_theorist = LinearRegression()


We initialize the SimpleCycle with the metadata describing the domain of the theory, the theorist, experimentalist and experiment runner, as well as a monitor which will let us know which cycle we're currently on.

>>> cycle = SimpleCycle(
...     theorist=example_theorist,
...     experimentalist=example_experimentalist,
...     experiment_runner=example_synthetic_experiment_runner,
...     monitor=lambda data: print(f"Generated {len(data.theories)} theories"),
... )
>>> cycle
<simple.SimpleCycle object at 0x...>


We can run the cycle by calling the run method:

>>> cycle.run(num_cycles=3)
Generated 1 theories
Generated 2 theories
Generated 3 theories
<simple.SimpleCycle object at 0x...>


We can now interrogate the results. The first set of conditions which went into the experiment runner were:

>>> cycle.data.conditions[0]
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])


The observations include the conditions and the results:

>>> cycle.data.observations[0]
array([[ 0.        ,  0.92675345],
[ 1.        ,  1.89519928],
[ 2.        ,  3.08746571],
[ 3.        ,  3.93023943],
[ 4.        ,  4.95429102],
[ 5.        ,  6.04763988],
[ 6.        ,  7.20770574],
[ 7.        ,  7.85681519],
[ 8.        ,  9.05735823],
[ 9.        , 10.18713406],
[10.        , 10.88517906]])


In the third cycle (index = 2) the first and last values are different again:

>>> cycle.data.observations[2][[0,-1]]
array([[ 0.        ,  1.08559827],
[10.        , 11.08179553]])


The best fit theory after the first cycle is:

>>> cycle.data.theories[0]
LinearRegression()

>>> def report_linear_fit(m: LinearRegression,  precision=4):
...     s = f"y = {np.round(m.coef_[0].item(), precision)} x " \
...     f"+ {np.round(m.intercept_.item(), 4)}"
...     return s
>>> report_linear_fit(cycle.data.theories[0])
'y = 1.0089 x + 0.9589'


The best fit theory after all the cycles, including all the data, is:

>>> report_linear_fit(cycle.data.theories[-1])
'y = 0.9989 x + 1.0292'


This is close to the ground truth theory of x -> (x + 1)

We can also run the cycle with more control over the execution flow:

>>> next(cycle)
Generated 4 theories
<simple.SimpleCycle object at 0x...>

>>> next(cycle)
Generated 5 theories
<simple.SimpleCycle object at 0x...>

>>> next(cycle)
Generated 6 theories
<simple.SimpleCycle object at 0x...>


We can continue to run the cycle as long as we like, with a simple arbitrary stopping condition like the number of theories generated:

>>> from itertools import takewhile
>>> _ = list(takewhile(lambda c: len(c.data.theories) < 9, cycle))
Generated 7 theories
Generated 8 theories
Generated 9 theories


... or the precision (here we keep iterating while the difference between the gradients of the second-last and last cycle is larger than 1x10^-3).

>>> _ = list(
...         takewhile(
...             lambda c: np.abs(c.data.theories[-1].coef_.item() -
...                            c.data.theories[-2].coef_.item()) > 1e-3,
...             cycle
...         )
...     )
Generated 10 theories
Generated 11 theories


... or continue to run as long as we like:

>>> _ = cycle.run(num_cycles=100)
Generated 12 theories
...
Generated 111 theories

##### Passing Static Parameters

It's easy to pass parameters to the cycle components, if there are any needed. Here we have an experimentalist which takes a parameter:

>>> uniform_random_rng = np.random.default_rng(180)
>>> def uniform_random_sampler(n):
...     return uniform_random_rng.uniform(low=0, high=11, size=n)
>>> example_experimentalist_with_parameters = make_pipeline([uniform_random_sampler])


The cycle can handle that using the params keyword:

>>> cycle_with_parameters = SimpleCycle(
...     theorist=example_theorist,
...     experimentalist=example_experimentalist_with_parameters,
...     experiment_runner=example_synthetic_experiment_runner,
...     params={"experimentalist": {"uniform_random_sampler": {"n": 7}}}
... )
>>> _ = cycle_with_parameters.run()
>>> cycle_with_parameters.data.conditions[-1].flatten()
array([6.33661987, 7.34916618, 6.08596494, 2.28566582, 1.9553974 ,
5.80023149, 3.27007909])


For the next cycle, if we wish, we can change the parameter value:

>>> cycle_with_parameters.params["experimentalist"]["uniform_random_sampler"]\
...     ["n"] = 2
>>> _ = cycle_with_parameters.run()
>>> cycle_with_parameters.data.conditions[-1].flatten()
array([10.5838232 ,  9.45666031])

##### Accessing "Cycle Properties"

Some experimentalists, experiment runners and theorists require access to the values created during the cycle execution, e.g. experimentalists which require access to the current best theory or the observed data. These data update each cycle, and so cannot easily be set using simple params.

For this case, it is possible to use "cycle properties" in the params dictionary. These are the following strings, which will be replaced during execution by their respective current values:

• "%observations.ivs[-1]%": the last observed independent variables
• "%observations.dvs[-1]%": the last observed dependent variables
• "%observations.ivs%": all the observed independent variables, concatenated into a single array
• "%observations.dvs%": all the observed dependent variables, concatenated into a single array
• "%theories[-1]%": the last fitted theorist
• "%theories%": all the fitted theorists

In the following example, we use the "observations.ivs" cycle property for an experimentalist which excludes those conditions which have already been seen.

>>> metadata_1 = VariableCollection(
...    independent_variables=[Variable(name="x1", allowed_values=range(10))],
...    dependent_variables=[Variable(name="y")],
...    )
>>> random_sampler_rng = np.random.default_rng(seed=180)
>>> def custom_random_sampler(conditions, n):
...     sampled_conditions = random_sampler_rng.choice(conditions, size=n, replace=False)
...     return sampled_conditions
>>> def exclude_conditions(conditions, excluded_conditions):
...     remaining_conditions = list(set(conditions) - set(excluded_conditions.flatten()))
...     return remaining_conditions
>>> unobserved_data_experimentalist = make_pipeline([
...     exclude_conditions,
...     custom_random_sampler
...     ]
... )
>>> cycle_with_cycle_properties = SimpleCycle(
...     theorist=example_theorist,
...     experimentalist=unobserved_data_experimentalist,
...     experiment_runner=example_synthetic_experiment_runner,
...     params={
...         "experimentalist": {
...             "exclude_conditions": {"excluded_conditions": "%observations.ivs%"},
...             "custom_random_sampler": {"n": 1}
...         }
...     }
... )


Now we can run the cycler to generate conditions and run experiments. The first time round, we have the full set of 10 possible conditions to select from, and we select "2" at random:

>>> _ = cycle_with_cycle_properties.run()
>>> cycle_with_cycle_properties.data.conditions[-1]
array([2])


We can continue to run the cycler, each time we add more to the list of "excluded" options:

>>> _ = cycle_with_cycle_properties.run(num_cycles=5)
>>> cycle_with_cycle_properties.data.conditions
[array([2]), array([6]), array([5]), array([7]), array([3]), array([4])]


By using the monitor callback, we can investigate what's going on with the cycle properties:

>>> cycle_with_cycle_properties.monitor = lambda data: print(
...     _get_cycle_properties(data)["%observations.ivs%"].flatten()
... )


The monitor evaluates at the end of each cycle and shows that we've added a new observed IV each step

>>> _ = cycle_with_cycle_properties.run()
[2. 6. 5. 7. 3. 4. 9.]
>>> _ = cycle_with_cycle_properties.run()
[2. 6. 5. 7. 3. 4. 9. 0.]


We deactivate the monitor by making it "None" again.

>>> cycle_with_cycle_properties.monitor = None


We can continue until we've sampled all of the options:

>>> _ = cycle_with_cycle_properties.run(num_cycles=2)
>>> cycle_with_cycle_properties.data.conditions
[array([2]), array([6]), array([5]), array([7]), array([3]),         array([4]), array([9]), array([0]), array([8]), array([1])]


If we try to evaluate it again, the experimentalist fails, as there aren't any more conditions which are available:

>>> cycle_with_cycle_properties.run()
Traceback (most recent call last):
...
ValueError: a cannot be empty unless no samples are taken

Source code in autora/cycle/simple.py
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Once initialized, the cycle can be started using the cycle.run method or by calling next(cycle). The .data attribute is updated with the results. Attributes: data (dataclass): an object which is updated during the cycle and has the following properties: - metadata - conditions: a list of np.ndarrays representing all of the IVs proposed by the experimentalist - observations: a list of np.ndarrays representing all of the IVs and DVs returned by the experiment runner - theories: a list of all the fitted theories (scikit-learn compatible estimators) params (dict): a nested dictionary with parameters for the cycle parts. { "experimentalist": {}, "theorist": {}, "experiment_runner": {} } Examples: ### Basic Usage Aim: Use the SimpleCycle to recover a simple ground truth theory from noisy data. >>> def ground_truth(x): ... return x + 1 The space of allowed x values is the integers between 0 and 10 inclusive, and we record the allowed output values as well. >>> from autora.variable import VariableCollection, Variable >>> metadata_0 = VariableCollection( ... independent_variables=[Variable(name="x1", allowed_values=range(11))], ... dependent_variables=[Variable(name="y", value_range=(-20, 20))], ... ) The experimentalist is used to propose experiments. Since the space of values is so restricted, we can just sample them all each time. >>> from autora.experimentalist.pipeline import make_pipeline >>> example_experimentalist = make_pipeline( ... [metadata_0.independent_variables[0].allowed_values]) When we run a synthetic experiment, we get a reproducible noisy result: >>> import numpy as np >>> def get_example_synthetic_experiment_runner(): ... rng = np.random.default_rng(seed=180) ... def runner(x): ... return ground_truth(x) + rng.normal(0, 0.1, x.shape) ... return runner >>> example_synthetic_experiment_runner = get_example_synthetic_experiment_runner() >>> example_synthetic_experiment_runner(np.ndarray([1])) array([2.04339546]) The theorist "tries" to work out the best theory. We use a trivial scikit-learn regressor. >>> from sklearn.linear_model import LinearRegression >>> example_theorist = LinearRegression() We initialize the SimpleCycle with the metadata describing the domain of the theory, the theorist, experimentalist and experiment runner, as well as a monitor which will let us know which cycle we're currently on. >>> cycle = SimpleCycle( ... metadata=metadata_0, ... theorist=example_theorist, ... experimentalist=example_experimentalist, ... experiment_runner=example_synthetic_experiment_runner, ... monitor=lambda data: print(f"Generated {len(data.theories)} theories"), ... ) >>> cycle # doctest: +ELLIPSIS We can run the cycle by calling the run method: >>> cycle.run(num_cycles=3) # doctest: +ELLIPSIS Generated 1 theories Generated 2 theories Generated 3 theories We can now interrogate the results. The first set of conditions which went into the experiment runner were: >>> cycle.data.conditions[0] array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) The observations include the conditions and the results: >>> cycle.data.observations[0] array([[ 0. , 0.92675345], [ 1. , 1.89519928], [ 2. , 3.08746571], [ 3. , 3.93023943], [ 4. , 4.95429102], [ 5. , 6.04763988], [ 6. , 7.20770574], [ 7. , 7.85681519], [ 8. , 9.05735823], [ 9. , 10.18713406], [10. , 10.88517906]]) In the third cycle (index = 2) the first and last values are different again: >>> cycle.data.observations[2][[0,-1]] array([[ 0. , 1.08559827], [10. , 11.08179553]]) The best fit theory after the first cycle is: >>> cycle.data.theories[0] LinearRegression() >>> def report_linear_fit(m: LinearRegression, precision=4): ... s = f"y = {np.round(m.coef_[0].item(), precision)} x " \\ ... f"+ {np.round(m.intercept_.item(), 4)}" ... return s >>> report_linear_fit(cycle.data.theories[0]) 'y = 1.0089 x + 0.9589' The best fit theory after all the cycles, including all the data, is: >>> report_linear_fit(cycle.data.theories[-1]) 'y = 0.9989 x + 1.0292' This is close to the ground truth theory of x -> (x + 1) We can also run the cycle with more control over the execution flow: >>> next(cycle) # doctest: +ELLIPSIS Generated 4 theories >>> next(cycle) # doctest: +ELLIPSIS Generated 5 theories >>> next(cycle) # doctest: +ELLIPSIS Generated 6 theories We can continue to run the cycle as long as we like, with a simple arbitrary stopping condition like the number of theories generated: >>> from itertools import takewhile >>> _ = list(takewhile(lambda c: len(c.data.theories) < 9, cycle)) Generated 7 theories Generated 8 theories Generated 9 theories ... or the precision (here we keep iterating while the difference between the gradients of the second-last and last cycle is larger than 1x10^-3). >>> _ = list( ... takewhile( ... lambda c: np.abs(c.data.theories[-1].coef_.item() - ... c.data.theories[-2].coef_.item()) > 1e-3, ... cycle ... ) ... ) Generated 10 theories Generated 11 theories ... or continue to run as long as we like: >>> _ = cycle.run(num_cycles=100) # doctest: +ELLIPSIS Generated 12 theories ... Generated 111 theories ### Passing Static Parameters It's easy to pass parameters to the cycle components, if there are any needed. Here we have an experimentalist which takes a parameter: >>> uniform_random_rng = np.random.default_rng(180) >>> def uniform_random_sampler(n): ... return uniform_random_rng.uniform(low=0, high=11, size=n) >>> example_experimentalist_with_parameters = make_pipeline([uniform_random_sampler]) The cycle can handle that using the params keyword: >>> cycle_with_parameters = SimpleCycle( ... metadata=metadata_0, ... theorist=example_theorist, ... experimentalist=example_experimentalist_with_parameters, ... experiment_runner=example_synthetic_experiment_runner, ... params={"experimentalist": {"uniform_random_sampler": {"n": 7}}} ... ) >>> _ = cycle_with_parameters.run() >>> cycle_with_parameters.data.conditions[-1].flatten() array([6.33661987, 7.34916618, 6.08596494, 2.28566582, 1.9553974 , 5.80023149, 3.27007909]) For the next cycle, if we wish, we can change the parameter value: >>> cycle_with_parameters.params["experimentalist"]["uniform_random_sampler"]\\ ... ["n"] = 2 >>> _ = cycle_with_parameters.run() >>> cycle_with_parameters.data.conditions[-1].flatten() array([10.5838232 , 9.45666031]) ### Accessing "Cycle Properties" Some experimentalists, experiment runners and theorists require access to the values created during the cycle execution, e.g. experimentalists which require access to the current best theory or the observed data. These data update each cycle, and so cannot easily be set using simple params. For this case, it is possible to use "cycle properties" in the params dictionary. These are the following strings, which will be replaced during execution by their respective current values: - "%observations.ivs[-1]%": the last observed independent variables - "%observations.dvs[-1]%": the last observed dependent variables - "%observations.ivs%": all the observed independent variables, concatenated into a single array - "%observations.dvs%": all the observed dependent variables, concatenated into a single array - "%theories[-1]%": the last fitted theorist - "%theories%": all the fitted theorists In the following example, we use the "observations.ivs" cycle property for an experimentalist which excludes those conditions which have already been seen. >>> metadata_1 = VariableCollection( ... independent_variables=[Variable(name="x1", allowed_values=range(10))], ... dependent_variables=[Variable(name="y")], ... ) >>> random_sampler_rng = np.random.default_rng(seed=180) >>> def custom_random_sampler(conditions, n): ... sampled_conditions = random_sampler_rng.choice(conditions, size=n, replace=False) ... return sampled_conditions >>> def exclude_conditions(conditions, excluded_conditions): ... remaining_conditions = list(set(conditions) - set(excluded_conditions.flatten())) ... return remaining_conditions >>> unobserved_data_experimentalist = make_pipeline([ ... metadata_1.independent_variables[0].allowed_values, ... exclude_conditions, ... custom_random_sampler ... ] ... ) >>> cycle_with_cycle_properties = SimpleCycle( ... metadata=metadata_1, ... theorist=example_theorist, ... experimentalist=unobserved_data_experimentalist, ... experiment_runner=example_synthetic_experiment_runner, ... params={ ... "experimentalist": { ... "exclude_conditions": {"excluded_conditions": "%observations.ivs%"}, ... "custom_random_sampler": {"n": 1} ... } ... } ... ) Now we can run the cycler to generate conditions and run experiments. The first time round, we have the full set of 10 possible conditions to select from, and we select "2" at random: >>> _ = cycle_with_cycle_properties.run() >>> cycle_with_cycle_properties.data.conditions[-1] array([2]) We can continue to run the cycler, each time we add more to the list of "excluded" options: >>> _ = cycle_with_cycle_properties.run(num_cycles=5) >>> cycle_with_cycle_properties.data.conditions [array([2]), array([6]), array([5]), array([7]), array([3]), array([4])] By using the monitor callback, we can investigate what's going on with the cycle properties: >>> cycle_with_cycle_properties.monitor = lambda data: print( ... _get_cycle_properties(data)["%observations.ivs%"].flatten() ... ) The monitor evaluates at the end of each cycle and shows that we've added a new observed IV each step >>> _ = cycle_with_cycle_properties.run() [2. 6. 5. 7. 3. 4. 9.] >>> _ = cycle_with_cycle_properties.run() [2. 6. 5. 7. 3. 4. 9. 0.] We deactivate the monitor by making it "None" again. >>> cycle_with_cycle_properties.monitor = None We can continue until we've sampled all of the options: >>> _ = cycle_with_cycle_properties.run(num_cycles=2) >>> cycle_with_cycle_properties.data.conditions # doctest: +NORMALIZE_WHITESPACE [array([2]), array([6]), array([5]), array([7]), array([3]), \ array([4]), array([9]), array([0]), array([8]), array([1])] If we try to evaluate it again, the experimentalist fails, as there aren't any more conditions which are available: >>> cycle_with_cycle_properties.run() # doctest: +ELLIPSIS Traceback (most recent call last): ... ValueError: a cannot be empty unless no samples are taken """ def __init__( self, metadata: VariableCollection, theorist, experimentalist, experiment_runner, monitor: Optional[Callable[[SimpleCycleData], None]] = None, params: Optional[Dict] = None, ): """ Args: metadata: a description of the dependent and independent variables theorist: a scikit-learn-compatible estimator experimentalist: an autora.experimentalist.Pipeline experiment_runner: a function to map independent variables onto observed dependent variables monitor: a function which gets read-only access to the data attribute at the end of each cycle. params: a nested dictionary with parameters to be passed to the parts of the cycle. E.g. if the experimentalist had a step named "pool" which took an argument "n", which you wanted to set to the value 30, then params would be set to this: {"experimentalist": {"pool": {"n": 30}}} """ self.theorist = theorist self.experimentalist = experimentalist self.experiment_runner = experiment_runner self.monitor = monitor if params is None: params = dict() self.params = params self.data = SimpleCycleData( metadata=metadata, conditions=[], observations=[], theories=[], ) def run(self, num_cycles: int = 1): for i in range(num_cycles): next(self) return self def __next__(self): assert ( "experiment_runner" not in self.params ), "experiment_runner cannot yet accept cycle properties" assert ( "theorist" not in self.params ), "theorist cannot yet accept cycle properties" data = self.data params_with_cycle_properties = _resolve_cycle_properties( self.params, _get_cycle_properties(self.data) ) data = self._experimentalist_callback( self.experimentalist, data, params_with_cycle_properties.get("experimentalist", dict()), ) data = self._experiment_runner_callback(self.experiment_runner, data) data = self._theorist_callback(self.theorist, data) self._monitor_callback(data) self.data = data return self def __iter__(self): return self @staticmethod def _experimentalist_callback( experimentalist: Pipeline, data_in: SimpleCycleData, params: dict ): new_conditions = experimentalist(**params) if isinstance(new_conditions, Iterable): # If the pipeline gives us an iterable, we need to make it into a concrete array. # We can't move this logic to the Pipeline, because the pipeline doesn't know whether # it's within another pipeline and whether it should convert the iterable to a # concrete array. new_conditions_values = list(new_conditions) new_conditions_array = np.array(new_conditions_values) else: raise NotImplementedError(f"Object {new_conditions} can't be handled yet.") assert isinstance( new_conditions_array, np.ndarray ) # Check the object is bounded data_out = replace( data_in, conditions=data_in.conditions + [new_conditions_array], ) return data_out @staticmethod def _experiment_runner_callback( experiment_runner: Callable, data_in: SimpleCycleData ): x = data_in.conditions[-1] y = experiment_runner(x) new_observations = np.column_stack([x, y]) data_out = replace( data_in, observations=data_in.observations + [new_observations] ) return data_out @staticmethod def _theorist_callback(theorist, data_in: SimpleCycleData): all_observations = np.row_stack(data_in.observations) n_xs = len( data_in.metadata.independent_variables ) # The number of independent variables x, y = all_observations[:, :n_xs], all_observations[:, n_xs:] if y.shape[1] == 1: y = y.ravel() new_theorist = copy.deepcopy(theorist) new_theorist.fit(x, y) data_out = replace( data_in, theories=data_in.theories + [new_theorist], ) return data_out def _monitor_callback(self, data: SimpleCycleData): if self.monitor is not None: self.monitor(data) 

### __init__(metadata, theorist, experimentalist, experiment_runner, monitor=None, params=None)

Parameters:

Name Type Description Default
metadata VariableCollection

a description of the dependent and independent variables

required
theorist

a scikit-learn-compatible estimator

required
experimentalist

an autora.experimentalist.Pipeline

required
experiment_runner

a function to map independent variables onto observed dependent variables

required
monitor Optional[Callable[[SimpleCycleData], None]]

a function which gets read-only access to the data attribute at the end of each cycle.

None
params Optional[Dict]

a nested dictionary with parameters to be passed to the parts of the cycle. E.g. if the experimentalist had a step named "pool" which took an argument "n", which you wanted to set to the value 30, then params would be set to this: {"experimentalist": {"pool": {"n": 30}}}

None
Source code in autora/cycle/simple.py
 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 def __init__( self, metadata: VariableCollection, theorist, experimentalist, experiment_runner, monitor: Optional[Callable[[SimpleCycleData], None]] = None, params: Optional[Dict] = None, ): """ Args: metadata: a description of the dependent and independent variables theorist: a scikit-learn-compatible estimator experimentalist: an autora.experimentalist.Pipeline experiment_runner: a function to map independent variables onto observed dependent variables monitor: a function which gets read-only access to the data attribute at the end of each cycle. params: a nested dictionary with parameters to be passed to the parts of the cycle. E.g. if the experimentalist had a step named "pool" which took an argument "n", which you wanted to set to the value 30, then params would be set to this: {"experimentalist": {"pool": {"n": 30}}} """ self.theorist = theorist self.experimentalist = experimentalist self.experiment_runner = experiment_runner self.monitor = monitor if params is None: params = dict() self.params = params self.data = SimpleCycleData( metadata=metadata, conditions=[], observations=[], theories=[], ) 

## SimpleCycleData dataclass

An object passed between and updated by processing steps in the SimpleCycle.

Source code in autora/cycle/simple.py
 14 15 16 17 18 19 20 21 22 23 24 25 26 27 @dataclass(frozen=True) class SimpleCycleData: """An object passed between and updated by processing steps in the SimpleCycle.""" # Static metadata: VariableCollection # Aggregates each cycle from the: # ... Experimentalist conditions: List[np.ndarray] # ... Experiment Runner observations: List[np.ndarray] # ... Theorist theories: List[BaseEstimator]